Remote sensing platforms such as satellites, drones, etc., often employ multiple sensors to observe a common subject. For instance, a satellite may employ not only a visible spectrum camera, but may also employ an infrared camera to observe portions of the earth. As technology allows for remote sensors to decrease in size, the reduced footprint of sensors can allow for more sensors to be placed on a given platform. However, oftentimes the multiple sensors must compete for bandwidth in order to communicate data to a receiver that can store and use the acquired data. In severely bandlimited channels, many remote sensing platforms may be unable to effectively transmit high-resolution data. Thus, as a given platform employs more sensors, in order for each sensor to transmit its data to a receiver, the data may have to be compressed, thus, leading to lower resolution data.
Because multiple sensors oftentimes are observing identical phenomenon (i.e., looking at the same portion of the earth), there may be a high degree of correlation among the images observed by each individual sensor on a multi-sensor platform. In an attempt to ease the burden on bandwidth-constrained channels, a decoder can be constructed that can take advantage of the correlation between the data collected by two independent sensors, allowing for asymmetric compression of sensor data, which, in turn, allows for optimal bandwidth performance over a given communications channel.
The system can employ a separate encoder for each source of data but on the receiver end employ a single decoder that utilizes distributed source coding that can maximally exploit dependence across sensors to dramatically reduce transmission requirements for a given sensor of a multi-sensor platform.